In this paper we proposed a source-free domain adaptation framework for unsupervised domain adaptive 3D object detectors that uses a transformer module to compute an attentive class prototype to perform pseudo-label refinement during self training. Our method outperforms other recent domain adaptation networks for several different domain shifts. We mainly address pseudo-label noise related to the false positive mis-classification of regions in the 3D scene, and not the dimensions of the bounding box. A factor that contributes the drop in performance upon domain shift is the difference in average size of the vehicles in different locations [1]}. While they address this by the weakly supervised approach of statistical normalization, in the future we hope to provide a fully unsupervised solution.
